Macroeconomic Impact and Benefit/Cost Analysis of Transportation and Mining Developments in the Northwest Territories
Bibliographic record
Abstract
This paper summarizes the results of two studies undertaken to quantify the macroeconomic impact and the benefits and costs associated with transportation and mineral development scenarios in the Northwest Territories.Each development scenario involves investment in a transportation corridor extending north from Yellowknife towards the Arctic Coast, and the staged development of various mineral deposits.The region north of Yellowknife, known as the Slave Geologic Province, is a storehouse for numerous gold, base metal and diamond reserves and is recognized as the premier new mineral region in Canada At the present time landbased transportation infrastructure consists of a privately constructed winter road extending from Yellowknife to the Lupin gold mine, approximately two-thirds of the distance to the Arctic Coast.In terms of the macroeconomic impact, the development scenarios analyzed would have a significant, positive impact on the economy.This impact would be significant not only in the Northwest Territories but also in southern Canada, since most of the goods and services consumed in the Northwest Territories are produced in other parts of Canada The analysis indicates that approximately one-third of the GDP impacts and three-quarters of the employment impacts would occur outside of the Northwest Territories, mainly in the provinces of Ontario, Alberta, Quebec and British Columbia.The benefit-cost analysis indicated a positive Net Present Value for each of the development scenarios.For the most optimistic scenario, the benefits exceeded the costs by $3 billion over the 20 year study period.The benefits are dominated by the value of the mineral output resulting from new mineral developments.Mining holds the best prospects for economic development in the North.Transportation infrastructure is essential for the development of mineral deposits.While a causal N eudorf/Hassan relationship between transportation infrastructure and mining activities was not assumed, the two studies reported here have demonstrated that providing transportation infrastructure for the development of mines produces significant macroeconomic impacts and that the benefits would exceed the costs.This information would be useful in discussions about funding transportation infrastructure projects in support of mineral developments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".